Time Series Forecasting
Diffusers
Safetensors
time-series
diffusion
scenario-generation
weather
multivariate-time-series
Eval Results (legacy)
Instructions to use kyLELEng/weather-scenario-diffusion-1d with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kyLELEng/weather-scenario-diffusion-1d with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("kyLELEng/weather-scenario-diffusion-1d", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
| { | |
| "_class_name": "UNet1DModel", | |
| "_diffusers_version": "0.37.1", | |
| "act_fn": "silu", | |
| "block_out_channels": [ | |
| 64, | |
| 128, | |
| 256, | |
| 512 | |
| ], | |
| "down_block_types": [ | |
| "DownBlock1DNoSkip", | |
| "DownBlock1D", | |
| "AttnDownBlock1D", | |
| "AttnDownBlock1D" | |
| ], | |
| "downsample_each_block": false, | |
| "extra_in_channels": 128, | |
| "flip_sin_to_cos": true, | |
| "freq_shift": 0.0, | |
| "in_channels": 63, | |
| "layers_per_block": 2, | |
| "mid_block_type": "UNetMidBlock1D", | |
| "norm_num_groups": 8, | |
| "out_block_type": null, | |
| "out_channels": 21, | |
| "sample_rate": null, | |
| "sample_size": 256, | |
| "time_embedding_dim": null, | |
| "time_embedding_type": "fourier", | |
| "up_block_types": [ | |
| "AttnUpBlock1D", | |
| "AttnUpBlock1D", | |
| "UpBlock1D", | |
| "UpBlock1DNoSkip" | |
| ], | |
| "use_timestep_embedding": false | |
| } | |